Pandas - For Data Analysis. Uses Structures and tables and time series Almost always called in with <lower case "pd">¶

Numpy - bade of scientific computing called in with lower case np¶

Matplotlib.pyplot - allows for static and animated vizualization. called in with lower case "plt".¶

Note: in order to have graphs properly show in jupyter lavs, we use %matplotlib inline command¶

seaborn - based on matplotli. provides attractive graphics. called "sns"¶

Data Structures¶

Lists: Demoted with [] and they can be appended, modified¶

Tuples: Denoted with () and they can't be changed or added to¶

Dictionaries- curly brackets{}¶

In [ ]:
In [68]:
## Import Packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import sklearn
import pip
import sys
!{sys.executable} -m pip install xgboost

Requirement already satisfied: xgboost in c:\users\user\anaconda3\lib\site-packages (3.0.5)
Requirement already satisfied: numpy in c:\users\user\anaconda3\lib\site-packages (from xgboost) (2.1.3)
Requirement already satisfied: scipy in c:\users\user\anaconda3\lib\site-packages (from xgboost) (1.15.3)
In [69]:
# Create Timestamp
from datetime import datetime
time_now = datetime.now().strftime('%m_%d_%Y_%I%M%p')
print("Current Time: {}".format(time_now))

Current Time: 09_15_2025_0911AM
In [70]:
#import warning filter - otherwise you'll get lots of warnings that dont affect the outcome
from warnings import simplefilter
#ignore all future warnings
simplefilter(action='ignore',category=FutureWarning)
In [71]:
df=pd.read_csv('diabetes.csv')
In [72]:
df.head()
Out[72]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1
In [73]:
df.describe()
Out[73]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000
mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 0.471876 33.240885 0.348958
std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 0.331329 11.760232 0.476951
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.078000 21.000000 0.000000
25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 0.243750 24.000000 0.000000
50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 0.372500 29.000000 0.000000
75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 0.626250 41.000000 1.000000
max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 2.420000 81.000000 1.000000
In [74]:
df.columns
Out[74]:
Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],
      dtype='object')
In [75]:
df.shape
Out[75]:
(768, 9)
In [76]:
#look at the relationship
sns.pairplot(df,hue="Outcome")
#plt.show()
Out[76]:
<seaborn.axisgrid.PairGrid at 0x1d6e59255b0>
In [77]:
#check for null values
print(df.isnull().values.any())
#if you have rows with missing date, use df=df.dropna() to get rid of rows null values

False
In [78]:
# we need to standardize the variable so that we can accurately use machine learning. our object is to have the dtandard deviations all between 0 and 1
X=df.drop('Outcome',axis=1)
y=df['Outcome']
#standardize variables
X=(X-X.mean())/X.std()
In [79]:
X.head()
Out[79]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age
0 0.639530 0.847771 0.149543 0.906679 -0.692439 0.203880 0.468187 1.425067
1 -0.844335 -1.122665 -0.160441 0.530556 -0.692439 -0.683976 -0.364823 -0.190548
2 1.233077 1.942458 -0.263769 -1.287373 -0.692439 -1.102537 0.604004 -0.105515
3 -0.844335 -0.997558 -0.160441 0.154433 0.123221 -0.493721 -0.920163 -1.040871
4 -1.141108 0.503727 -1.503707 0.906679 0.765337 1.408828 5.481337 -0.020483
In [80]:
X.describe()
Out[80]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age
count 7.680000e+02 7.680000e+02 7.680000e+02 7.680000e+02 7.680000e+02 7.680000e+02 7.680000e+02 7.680000e+02
mean -6.938894e-17 -1.156482e-17 1.272131e-17 9.367507e-17 -1.676899e-17 2.613650e-16 2.359224e-16 2.104798e-16
std 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
min -1.141108e+00 -3.781190e+00 -3.570271e+00 -1.287373e+00 -6.924393e-01 -4.057829e+00 -1.188778e+00 -1.040871e+00
25% -8.443348e-01 -6.847901e-01 -3.670975e-01 -1.287373e+00 -6.924393e-01 -5.951906e-01 -6.885198e-01 -7.857741e-01
50% -2.507887e-01 -1.218083e-01 1.495433e-01 1.544326e-01 -4.277835e-01 9.413653e-04 -2.999328e-01 -3.606124e-01
75% 6.395305e-01 6.053764e-01 5.628560e-01 7.186174e-01 4.117396e-01 5.843897e-01 4.659233e-01 6.597757e-01
max 3.904034e+00 2.442886e+00 2.732747e+00 4.918660e+00 6.648507e+00 4.452906e+00 5.879733e+00 4.061069e+00
In [81]:
# Split data into 75% training and 25% learning
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Assistant¶

The error occurs because the variables X and y are not defined before being used in the train_test_split function. You need to define these variables before splitting the data.

Would you like me to provide the corrected code?

In [82]:
#logistical regression
from sklearn.linear_model import LogisticRegression
lr=LogisticRegression()
lr.fit(X_train,y_train)
Out[82]:
LogisticRegression()
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LogisticRegression()
In [92]:
#LR predictions
lr_preds=lr.predict(X_test)
lr_preds
Out[92]:
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,
       0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1,
       0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1])
In [84]:
# use Decision Tree Model
from sklearn.tree import DecisionTreeClassifier
dt= DecisionTreeClassifier()
dt.fit(X_train,y_train)
Out[84]:
DecisionTreeClassifier()
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DecisionTreeClassifier()
In [85]:
#dt predictions
dt_preds=dt.predict(X_test)
dt_preds
Out[85]:
array([1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,
       0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1,
       0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
       1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0,
       1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0,
       0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0])
In [86]:
#random Forest Classifier
from sklearn.ensemble import RandomForestClassifier
rf=RandomForestClassifier()
rf.fit(X_train,y_train)
Out[86]:
RandomForestClassifier()
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RandomForestClassifier()
In [87]:
#rf Predicitons
rf_preds=rf.predict(X_test)
rf_preds
Out[87]:
array([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,
       0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1,
       0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,
       0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,
       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0,
       0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1])
In [88]:
#XG boost
from xgboost import XGBClassifier
xgb=XGBClassifier()
xgb.fit(X_train,y_train)
Out[88]:
XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              feature_weights=None, gamma=None, grow_policy=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_bin=None, max_cat_threshold=None,
              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
              max_leaves=None, min_child_weight=None, missing=nan,
              monotone_constraints=None, multi_strategy=None, n_estimators=None,
              n_jobs=None, num_parallel_tree=None, ...)
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XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              feature_weights=None, gamma=None, grow_policy=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_bin=None, max_cat_threshold=None,
              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
              max_leaves=None, min_child_weight=None, missing=nan,
              monotone_constraints=None, multi_strategy=None, n_estimators=None,
              n_jobs=None, num_parallel_tree=None, ...)
In [89]:
#XGB preditions
xgb_preds=xgb.predict(X_test)
xgb_preds
Out[89]:
array([1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,
       0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1,
       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,
       0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1,
       1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1])
In [94]:
#evaluate the accuracy of the models
model=np.array(['LogisticRegression','DecisionTree','Random Forest','XGBoost'])
from sklearn.metrics import accuracy_score
scores = np.array([accuracy_score(lr_preds,y_test),accuracy_score(dt_preds,y_test),accuracy_score(rf_preds,y_test),accuracy_score(xgb_preds,y_test)])
In [97]:
df= {'model':model,'scores':scores}
sns.barplot(x='model',y='scores',data=df)
plt.show()
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In [102]:
#Lest's assume we have new data set with no outcome that we want to predict.
df1=pd.read_csv('diabetes.csv')
base_data=df1.sample(frac=.25)
base_data=df1.drop(columns=["Outcome"])
base_data.head()
lr_preds=lr.predict(base_data)
dt_preds=dt.predict(base_data)
rf_preds=rf.predict(base_data)
xgb_preds=xgb.predict(base_data)
In [103]:
#add predicted data to diabetes file with outcome column for comparision
df1["Logistic Regression"]=lr_preds
df1["Decision Tree"]=dt_preds
df1["Random Forest"]=rf_preds
df1["XGBoost"]=xgb_preds
df1.head()
Out[103]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Logistic Regression Decision Tree Random Forest XGBoost
0 6 148 72 35 0 33.6 0.627 50 1 1 1 1 1
1 1 85 66 29 0 26.6 0.351 31 0 1 1 1 1
2 8 183 64 0 0 23.3 0.672 32 1 1 1 1 1
3 1 89 66 23 94 28.1 0.167 21 0 1 1 1 1
4 0 137 40 35 168 43.1 2.288 33 1 1 1 1 1
In [104]:
#export new file to excel
df1.to_excel('Diabetes with 4 ML predictions.xlsx')
In [ ]: